1. Field of the Invention
The present invention relates to computerized time-lapse image analysis and more particularly to a recipe station framework to support intuitive workflow that starts with incremental recipe execution, continuous result monitoring with image guided data analysis/data guided image visualization, recipe fine-tuning, and mask and track editing.
2. Description of the Related Art
a. Description of Problem that Motivated Invention
The technology advancement has enabled the routine acquisition of movie (image sequences) from not only video cameras but also smart phones. Therefore, the demand for time-lapse (rather than fixed point) image analysis becomes more prevalent. In the bioscience field, the advent of time-lapse microscopy and live cell fluorescence probes has enabled biologists to visualize the inner working of living cells in their natural context. Expectations are high for breakthroughs in area such as cell response and motility modification by drugs, control of targeted sequence incorporation into the chromatin for cell therapy, spatial-temporal organization of the cells and its changes with time or under infection, assessment of pathogens routing into the cell, interaction between proteins, and sanitary control of pathogen evolution, etc. The breakthroughs could revolutionize the broad fields in basic research, drug discovery and disease diagnosis.
Deciphering the complex machinery of cell function and dysfunction necessitates a detailed understanding of the dynamics of proteins, organelles, and cell populations. Due to the complexity of the time-lapse image analysis tasks to cover the wide range of highly variable and intricate properties of biological material, it is difficult to have fully automated solutions except some dedicated high-volume applications such as cancer screening, wafer defect inspection. Most of the computerized image analysis applications require interactive confirmation, editing and data analysis by users.
After tackling the huge complexities involved in establishing a live cell imaging study, scientists are often frustrated by the difficulties of image quantification that requires either tedious manual operations or specialized image processing and programming skills to achieve the desired outcomes. It is highly desirable to have an intuitive, easy-to-use workflow for obtaining optimal time-lapse analysis outcomes and efficient result viewing and sharing without specialized image processing and programming knowledge.
b. How Did Prior Art Handle the Problem?
The prior art approach provides manual analysis tools or manual editing tools. However, the tools become impractical for time-lapse image analysis, as the data volume is high and the errors could accumulate over time. For example, in tracking applications of time-lapse image sequence, a wrong track assignment in an early time frame will propagate to the later time frames. This causes significant inefficiency for a user to review and correct the mistakes, as the same mistakes have to be repeatedly corrected.
Furthermore, for a meaningful spatial-temporal analysis, the time-lapse image sequence has to cover a long time duration which has high data volume that requires timely review and timely correction of analysis error or timely updates of the processing instructions (recipes) to achieve good outcome efficiently. The existing tools do not facilitate the above requirements.
Therefore, a more sophisticated computerized framework and method for time-lapse image analysis is urgently needed to address the deficiencies of the prior art methods.